A Machine Learning Approach to Evaluate Variables of Math Anxiety in STEM Students

Abstract

The relationships between math anxiety and other variables such as students’ motivation and confidence have been extensively studied. The main purpose of the present study was to employ a machine learning approach to provide a deeper understanding of variables associated with math anxiety. Specifically, we applied classification and regression tree models to weekly survey data of science, technology, engineering, and mathematics (STEM) students enrolled in calculus. The tree models accurately identified that the level of confidence is the primary predictor of math anxiety. Students with low levels of confidence expressed high levels of math anxiety. The academic level of students and the number of weekly hours studied were the next two predictors of math anxiety. The junior and senior students had lower math anxiety. Also, those with a higher number of hours studied were generally less anxious. Weekly tree diagrams provided a detailed analysis of the interrelations between math anxiety and variables including academic level, number of hours studied, gender, motivation, and confidence. We noticed that the nature of such interrelations can change during the semester. For instance, in the first week of the semester, confidence was the primary factor, followed by academic level and then motivation. However, in the third week, the order of the interrelation changed to confidence, academic level, and course level, respectively. In summary, decision tree models can be used to predict math anxiety and to provide a more detailed analysis of data associated with math anxiety.